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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/145944


    Title: 使用隨機漫步的監督式學習
    Random Walk-based Supervised Learning
    Authors: 羅嘉承
    Lo, Chia-Cheng
    Contributors: 周珮婷
    陳怡如
    羅嘉承
    Lo, Chia-Cheng
    Keywords: 監督式學習
    分類
    相似度
    隨機漫步
    馬可夫鏈
    OutRank
    Supervised Learning
    Classification
    Similarity
    Random walk
    Markov Chain
    OutRank
    Date: 2023
    Issue Date: 2023-07-06 17:05:14 (UTC+8)
    Abstract: OutRank 原是一種基於對像相似性所進行的異常偵測方法。不同於
    以距離或是密度來偵測的形式，OutRank 以計算資料點間的相似性，
    來找出在資料中的異常小族群。本論文延伸此概念，擴展應用到分
    類、監督式學習的問題上。根據 OutRank 的性質，我們可以得到各筆
    資料間的相似度，因此我們假設同一族群間的相似度會較接近。在本
    論文中，我們會針對不同的資料去做驗證，並且與經典的分類方法 :
    Random Forest 去做比較。
    OutRank was originally developed as an anomaly detection method based on object similarity. Unlike distance or density-based detection approaches, OutRank calculates the similarity between data points to identify small anomaly groups within the data. This study extends the concept of OutRank and applies it to classification and supervised learning problems. Based on the nature of OutRank, we assume that the similarity between data points within the same group will be higher. In this study,we verify this assumption using different datasets and compare the results with the classic classification method, Random Forest.
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    Description: 碩士
    國立政治大學
    統計學系
    110354010
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110354010
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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